Themed collection Computational protein design and structure prediction: Celebrating the 2024 Nobel Prize in Chemistry
Unlocking novel therapies: cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning
Proposed de novo peptide design strategy against amyloidogenic targets. After initial computational preparation of the binder and target, the computational and experimental validation are incorporated in iterative machine learning powered cycles to generate better and improved peptide-based targets.
Chem. Commun., 2024,60, 632-645
https://doi.org/10.1039/D3CC04630C
Role of conformational dynamics in the evolution of novel enzyme function
Enzymes exist as a dynamic ensemble of conformations, each potentially playing a key role in substrate binding, the chemical transformation, or product release. We discuss recent advances in the evaluation of the enzyme conformational dynamics and its evolution towards new functions or substrate preferences.
Chem. Commun., 2018,54, 6622-6634
https://doi.org/10.1039/C8CC02426J
Architecture of full-length type I modular polyketide synthases revealed by X-ray crystallography, cryo-electron microscopy, and AlphaFold2
Structures of intact polyketide synthase modules reveal conformational rearrangements and suggest asynchronous use of reaction chambers.
Nat. Prod. Rep., 2024,41, 1219-1234
https://doi.org/10.1039/D3NP00060E
Navigating the landscape of enzyme design: from molecular simulations to machine learning
Efficiently harnessing big data by combining molecular modelling and machine learning accelerates rational enzyme design for its applications in fine chemical synthesis and waste valorization, to address global environmental issues and sustainable development.
Chem. Soc. Rev., 2024,53, 8202-8239
https://doi.org/10.1039/D4CS00196F
A roadmap for metagenomic enzyme discovery
Shotgun metagenomic approaches to uncover new enzymes are underdeveloped relative to PCR- or activity-based functional metagenomics. Here we review computational and experimental strategies to discover biosynthetic enzymes from metagenomes.
Nat. Prod. Rep., 2021,38, 1994-2023
https://doi.org/10.1039/D1NP00006C
Coiled coil protein origami: from modular design principles towards biotechnological applications
This review illustrates the current state in designing coiled-coil-based proteins with an emphasis on coiled coil protein origami structures and their potential.
Chem. Soc. Rev., 2018,47, 3530-3542
https://doi.org/10.1039/C7CS00822H
Protein thermostability engineering
Using structure and sequence based analysis we can engineer proteins to increase their thermal stability.
RSC Adv., 2016,6, 115252-115270
https://doi.org/10.1039/C6RA16992A
Strategies for designing biocatalysts with new functions
Enzymes can be optimized to accelerate chemical transformations via a range of methods. In this review, we showcase how protein engineering and computational design techniques can be interfaced to develop highly efficient and selective biocatalysts.
Chem. Soc. Rev., 2024,53, 2851-2862
https://doi.org/10.1039/D3CS00972F
Computational design of orthogonal nucleoside kinases
Rosetta design software was employed to remodel the substrate specificity of Drosophila melanogaster 2′-deoxyribonucleoside kinase for efficient phosphorylation of the nucleoside analog prodrug 3′-deoxythymidine.
Chem. Commun., 2010,46, 8803-8805
https://doi.org/10.1039/C0CC02961K
De novo design of peptides that bind specific conformers of α-synuclein
De novo designed peptides bind specific conformers of α-synuclein fibrils.
Chem. Sci., 2024,15, 8414-8421
https://doi.org/10.1039/D3SC06245G
Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins
Hydrogen atom transfer (HAT) reactions, as they occur in many biological systems, are here predicted by machine learning.
Chem. Sci., 2024,15, 2518-2527
https://doi.org/10.1039/D3SC03922F
Tidying up the conformational ensemble of a disordered peptide by computational prediction of spectroscopic fingerprints
Pairing experiments with simulations, we predict spectroscopic fingerprints, enhancing understanding of disordered peptides' conformational ensembles. This helps rationalize elusive structure-spectra relationships for these peptides and proteins.
Chem. Sci., 2023,14, 8483-8496
https://doi.org/10.1039/D3SC02202A
Combining structural and coevolution information to unveil allosteric sites
Structure-based three-parameter model that integrates local binding site information, coevolutionary information, and information on dynamic allostery to identify potentially hidden allosteric sites in ensembles of protein structures.
Chem. Sci., 2023,14, 7057-7067
https://doi.org/10.1039/D2SC06272K
Thermodynamic origins of two-component multiphase condensates of proteins
We develop a computational method integrating a genetic algorithm with a residue-level coarse-grained model of intrinsically disordered proteins in order to uncover the molecular origins of multiphase condensates and enable their controlled design.
Chem. Sci., 2023,14, 1820-1836
https://doi.org/10.1039/D2SC05873A
AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor
A novel CDK20 small molecule inhibitor discovered by artificial intelligence based on an AlphaFold-predicted structure demonstrates the first application of AlphaFold in hit identification for efficient drug discovery.
Chem. Sci., 2023,14, 1443-1452
https://doi.org/10.1039/D2SC05709C
Protein quaternary structures in solution are a mixture of multiple forms
Comparing the different methods for determining oligomerization composition of a protein in solution at different concentrations. The ruler of μg ml−1 represents protein concentrations applicable for the different methods.
Chem. Sci., 2022,13, 11680-11695
https://doi.org/10.1039/D2SC02794A
From peptides to proteins: coiled-coil tetramers to single-chain 4-helix bundles
Rules for designing 4-helix bundles are defined, tested, and used to generate de novo peptide assemblies and a single-chain protein.
Chem. Sci., 2022,13, 11330-11340
https://doi.org/10.1039/D2SC04479J
Computationally driven discovery of SARS-CoV-2 Mpro inhibitors: from design to experimental validation
The dominant binding mode of the QUB-00006-Int-07 main protease inhibitor during absolute binding free energy simulations.
Chem. Sci., 2022,13, 3674-3687
https://doi.org/10.1039/D1SC05892D
Generating 3D molecules conditional on receptor binding sites with deep generative models
We generate 3D molecules conditioned on receptor binding sites by training a deep generative model on protein–ligand complexes. Our model uses the conditional receptor information to make chemically relevant changes to the generated molecules.
Chem. Sci., 2022,13, 2701-2713
https://doi.org/10.1039/D1SC05976A
Generation of bright monomeric red fluorescent proteins via computational design of enhanced chromophore packing
We used computational design to increase quantum yield in a fluorescent protein by optimizing chromophore packing to reduce non-radiative decay, resulting in an >10-fold increase in quantum yield that was further improved by directed evolution.
Chem. Sci., 2022,13, 1408-1418
https://doi.org/10.1039/D1SC05088E
Alchemical absolute protein–ligand binding free energies for drug design
Molecular dynamics based absolute protein–ligand binding free energies can be calculated accurately and at large scale to facilitate drug discovery.
Chem. Sci., 2021,12, 13958-13971
https://doi.org/10.1039/D1SC03472C
Prediction and mitigation of mutation threats to COVID-19 vaccines and antibody therapies
Antibody therapeutics and vaccines are among our last resort to end the raging COVID-19 pandemic.
Chem. Sci., 2021,12, 6929-6948
https://doi.org/10.1039/D1SC01203G
Computational strategy for intrinsically disordered protein ligand design leads to the discovery of p53 transactivation domain I binding compounds that activate the p53 pathway
A hierarchical computational strategy for IDP drug virtual screening (IDPDVS) was proposed and successfully applied to identify compounds that bind p53 TAD1 and restore wild-type p53 function in cancer cells.
Chem. Sci., 2021,12, 3004-3016
https://doi.org/10.1039/D0SC04670A
Discovery of cryptic allosteric sites using reversed allosteric communication by a combined computational and experimental strategy
Using reversed allosteric communication, we performed MD simulations, MSMs, and mutagenesis experiments, to discover allosteric sites. It reproduced the known allosteric site for MDL-801 on Sirt6 and uncovered a novel cryptic allosteric Pocket X.
Chem. Sci., 2021,12, 464-476
https://doi.org/10.1039/D0SC05131D
Enhancing a de novo enzyme activity by computationally-focused ultra-low-throughput screening
De novo enzymes capable of efficiently catalysis of a non-natural reaction are obtained through minimalist design plus computationally-focused variant library screening.
Chem. Sci., 2020,11, 6134-6148
https://doi.org/10.1039/D0SC01935F
DEEPScreen: high performance drug–target interaction prediction with convolutional neural networks using 2-D structural compound representations
The DEEPScreen system is composed of 704 target protein specific prediction models, each independently trained using experimental bioactivity measurements against many drug candidate small molecules, and optimized according to the binding properties of the target proteins.
Chem. Sci., 2020,11, 2531-2557
https://doi.org/10.1039/C9SC03414E
Remodeling a β-peptide bundle
We apply the Rosetta algorithm to repack the hydrophobic core of a β-peptide bundle while retaining both structure and stability.
Chem. Sci., 2013,4, 319-324
https://doi.org/10.1039/C2SC21117C
Computational study of the mechanism of a polyurethane esterase A (PueA) from Pseudomonas chlororaphis
We investigate the possible molecular mechanism of polyurethane esterase A, previously identified as responsible for degradation of a polyester polyurethane sample in Pseudomonas chlororaphis.
Faraday Discuss., 2024,252, 323-340
https://doi.org/10.1039/D4FD00022F
Dual inhibitory potential of ganoderic acid A on GLUT1/3: computational and in vitro insights into targeting glucose metabolism in human lung cancer
Human glucose transporters (GLUTs) facilitate the uptake of hexoses into cells. In cancer, the increased proliferation necessitates higher expression of GLUTs. This study demonstrates the inhibitory function of ganoderic acid A (GAA) on GLUT1/3.
RSC Adv., 2024,14, 28569-28584
https://doi.org/10.1039/D4RA04454A
Reaction mechanism and regioselectivity of uridine diphosphate glucosyltransferase RrUGT3: a combined experimental and computational study
A substrate binding induced conformational change was found to be essential for the occurrence of RrUGT3 catalyzed transglycosylation reactions.
Catal. Sci. Technol., 2024,14, 4882-4895
https://doi.org/10.1039/D4CY00721B
ProtAgents: protein discovery via large language model multi-agent collaborations combining physics and machine learning
ProtAgents is a de novo protein design platform based on multimodal LLMs, where distinct AI agents with expertise in knowledge retrieval, protein structure analysis, physics-based simulations, and results analysis tackle tasks in a dynamic setting.
Digital Discovery, 2024,3, 1389-1409
https://doi.org/10.1039/D4DD00013G
Exploring conformational landscapes and binding mechanisms of convergent evolution for the SARS-CoV-2 spike Omicron variant complexes with the ACE2 receptor using AlphaFold2-based structural ensembles and molecular dynamics simulations
. AlphaFold-based approaches for prediction of protein states and molecular dynamics simulations are integrated to characterize conformational ensembles and binding mechanisms of the SARS-CoV-2 spike Omicron variants with the host receptor ACE2.
Phys. Chem. Chem. Phys., 2024,26, 17720-17744
https://doi.org/10.1039/D4CP01372G
Interface design of SARS-CoV-2 symmetrical nsp7 dimer and machine learning-guided nsp7 sequence prediction reveals physicochemical properties and hotspots for nsp7 stability, adaptation, and therapeutic design
The study investigates the molecular intricacies of SARS-CoV-2 RdRp via computational protein design, machine learning, and structural analyses, shedding light on mutational selection events impacting viral evolution and therapeutic strategies.
Phys. Chem. Chem. Phys., 2024,26, 14046-14061
https://doi.org/10.1039/D4CP01014K
Molecularly imprinted nanoparticles reveal regulatory scaffolding features in Pyk2 tyrosine kinase
We employ peptide-binding molecularly imprinted nanoparticles (MINPs) to probe the regulatory conformations and scaffolding interactions governing Pyk2 kinase activation.
RSC Chem. Biol., 2024,5, 447-453
https://doi.org/10.1039/D3CB00228D
PIGNet2: a versatile deep learning-based protein–ligand interaction prediction model for binding affinity scoring and virtual screening
PIGNet2, a versatile protein–ligand interaction prediction model that performs well in both molecule identification and optimization, demonstrates its potential in early-stage drug discovery.
Digital Discovery, 2024,3, 287-299
https://doi.org/10.1039/D3DD00149K
Helix-based screening with structure prediction using artificial intelligence has potential for the rapid development of peptide inhibitors targeting class I viral fusion
Peptide inhibitors against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are designed using a screening system for peptide-based inhibitors containing an α-helix region (SPICA) and structures predicted by AlphaFold2.
RSC Chem. Biol., 2024,5, 131-140
https://doi.org/10.1039/D3CB00166K
Thermostable protein-stabilized gold nanoclusters as a peroxidase mimic
By using a genetically modified thermostable protein (KTQ5C), we have synthesized protein-stabilized goldnanoclusters (AuNC@KTQ5C) with advantageous properties, such as heat stable fluorescent emission and heat resistant peroxidase-like activity.
Nanoscale Adv., 2023,5, 6061-6068
https://doi.org/10.1039/D3NA00566F
Computational thermostability engineering of a nitrile hydratase using synergetic energy and correlated configuration for redesigning enzymes (SECURE) strategy
A computational strategy using synergetic energy and correlated configuration for redesigning enzymes (SECURE) is proposed for the thermostability engineering of multimeric proteins.
Catal. Sci. Technol., 2023,13, 5880-5891
https://doi.org/10.1039/D3CY01102J
A deep learning model for type II polyketide natural product prediction without sequence alignment
Utilizing a large protein language model, we have formulated a deep learning framework designed for predicting type II polyketide natural products.
Digital Discovery, 2023,2, 1484-1493
https://doi.org/10.1039/D3DD00107E
Benchmarking protein structure predictors to assist machine learning-guided peptide discovery
Machine learning models provide an informed and efficient strategy to create novel peptide and protein sequences with the desired profiles.
Digital Discovery, 2023,2, 981-993
https://doi.org/10.1039/D3DD00045A
Predicting small molecule binding pockets on diacylglycerol kinases using chemoproteomics and AlphaFold
We provide a family-wide assessment of accessible sites for covalent targeting that combined with AlphaFold revealed predicted small molecule binding pockets for guiding future inhibitor development of the DGK superfamily.
RSC Chem. Biol., 2023,4, 422-430
https://doi.org/10.1039/D3CB00057E
3DProtDTA: a deep learning model for drug-target affinity prediction based on residue-level protein graphs
We propose a new deep learning DTA model 3DProtDTA, which utilises AlphaFold structure predictions in conjunction with the graph representation of proteins.
RSC Adv., 2023,13, 10261-10272
https://doi.org/10.1039/D3RA00281K
Virtual screening and activity evaluation of human uric acid transporter 1 (hURAT1) inhibitors
Alphafold2 was used to predict URAT1 protein structure, then the docking sites were identified, and three hit compounds were obtained through virtual screening and bioactivity verification.
RSC Adv., 2023,13, 3474-3486
https://doi.org/10.1039/D2RA07193B
The impact of AlphaFold2 on experimental structure solution
AlphaFold2 predicts protein folds from sequence, which can be used for experimental structural biology, in construction and de novo protein design, prediction of complexes and perhaps even effects of mutations and conformational space exploration.
Faraday Discuss., 2022,240, 184-195
https://doi.org/10.1039/D2FD00072E
Parallelized identification of on- and off-target protein interactions
Yeast surface display using multi target selections enables monitoring of specificity profiles for thousands of proteins in parallel.
Mol. Syst. Des. Eng., 2020,5, 349-357
https://doi.org/10.1039/C9ME00118B
In silico functional and tumor suppressor role of hypothetical protein PCNXL2 with regulation of the Notch signaling pathway
Since the last decade, various genome sequencing projects have led to the accumulation of an enormous set of genomic data; however, numerous protein-coding genes still need to be functionally characterized.
RSC Adv., 2018,8, 21414-21430
https://doi.org/10.1039/C8RA00589C
Comprehensive evaluation of ten docking programs on a diverse set of protein–ligand complexes: the prediction accuracy of sampling power and scoring power
We evaluated the capabilities of ten molecular docking programs to predict the ligand binding poses (sampling power) and rank the binding affinities (scoring power).
Phys. Chem. Chem. Phys., 2016,18, 12964-12975
https://doi.org/10.1039/C6CP01555G
Accelerated electron transport from photosystem I to redox partners by covalently linked ferredoxin
Tethering ferredoxin (PetF) to photosystem I increased light-induced PetF-mediated electron transfer to soluble acceptors. Tethering was equivalent to using a ten-to-one molar ratio of soluble PetF to PSI.
Phys. Chem. Chem. Phys., 2013,15, 19608-19614
https://doi.org/10.1039/C3CP53264J
π–π and cation–π interactions in protein–porphyrin complex crystal structures
We have described the π–π and cation–π interactions between the porphyrin ring and the protein part of porphyrin-containing proteins to better understand their stabilizing role.
RSC Adv., 2012,2, 12963-12972
https://doi.org/10.1039/C2RA21937A
About this collection
This cross-journal collection celebrates the 2024 Nobel Prize in Chemistry by bringing together research published on computational protein design and protein structure prediction. Nobel Laureates Demis Hassabis and John M. Jumper have successfully used artificial intelligence to predict the structure of almost all known proteins, and Nobel Laureate David Baker has used this technology to design and create entirely new proteins. This collection highlights work on protein design and analysis using computational methods, providing applications in biocatalysis, drug design and more.